Occlusion-aware methods.
Several methods have been proposed to deal with the challenging scenarios where human bodies are partially truncated or occluded.
Muller et al. [1]} and Hassan et al. [2]} introduce explicit modeling of human body self-contact and human-scene interactions, respectively.
These methods require ground-truth annotations which are hard to obtain.
Other methods leverage human-centric heatmaps, part segmentation masks, or dense UV estimations [3]}, to increase the model robustness on truncated images [4]}, crowded scenes (occluded by other people) [5]} or general occlusions [6]}, [7]}, [8]}, [9]}.
Although effective in particular scenarios, most of them directly regress SMPL parameters which still suffer from the limited representation strength.
To the best our our knowledge, the proposed VisDB representation is the first to explicitly model dense human body visibility (including truncation and all occlusion scenarios), which is trained with pseudo ground-truth visibility labels from dense UV estimates.
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